Зміст курсу

Introduction to Scikit Learn

1. The Very First Steps

Loading Data Sets SimpleImputer Ordinal Encoding

2. Scaling Numerical Data

MinMaxScaler MaxAbsScaler StandardScaler

3. Models in Scikit Learn

Prepearing Data Set 1/2 Prepearing Data Set 2/2

Loading Data Sets

Scikit-learn is one of the most widely used Python packages for Data Science and Machine Learning. Scikit-learn is a library implementing the algorithms for Supervised Learning and Unsupervised Learning. First of all, to work in Data Science you have to work with data. So, you have to get the data from somewhere.

One of the advantages of this library is that it already contains some basic datasets that can be used immediately. For this there are special functions in Scikit-learn. Let's look at them.

Let's get data about people with diabets. For this dataset there is load_diabetes() function in the Scikit-learn library.


              12345678
            
# Importing needed function from library
from sklearn.datasets import load_boston
# Using function fo get data about diabets from library
X, y = load_diabetes(return_X_y = True, as_frame = True)
# Displaying independent variables
print(X)
# Dislpaying dependent variable
print(y)

Here, we have very important bool parameters: return_X_y and as_frame. Exaplain.

Let's take a look at the another popular dataset about iris flowers and their features. See the example below.


              12345678
            
# Importing needed function from library
from sklearn.datasets import load_iris
# Using function fo get data about flowers from library
X, y = load_iris(return_X_y = True, as_frame = True)
# Displaying independent variables
print(X)
# Dislpaying dependent variable
print(y)

Завдання

You have to load_wine function from sklearn library.
You have to use imported function to get the data about properties and qualities of wines.
Please, use very important parameters in the right way:

you have to get the data, whica are divided by dependent and independetn variables;
you have to get this data in the data frame format.

Display independent variables(X) and dependent variable(y) on the screen.

Перейдіть на комп'ютер для реальної практикиПродовжуйте з того місця, де ви зупинились, використовуючи один з наведених нижче варіантів

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 1. Розділ 1

Loading Data Sets

One of the advantages of this library is that it already contains some basic datasets that can be used immediately. For this there are special functions in Scikit-learn. Let's look at them.

Let's get data about people with diabets. For this dataset there is load_diabetes() function in the Scikit-learn library.


              12345678
            
# Importing needed function from library
from sklearn.datasets import load_boston
# Using function fo get data about diabets from library
X, y = load_diabetes(return_X_y = True, as_frame = True)
# Displaying independent variables
print(X)
# Dislpaying dependent variable
print(y)

Here, we have very important bool parameters: return_X_y and as_frame. Exaplain.

Let's take a look at the another popular dataset about iris flowers and their features. See the example below.


              12345678
            
# Importing needed function from library
from sklearn.datasets import load_iris
# Using function fo get data about flowers from library
X, y = load_iris(return_X_y = True, as_frame = True)
# Displaying independent variables
print(X)
# Dislpaying dependent variable
print(y)

Завдання

You have to load_wine function from sklearn library.
You have to use imported function to get the data about properties and qualities of wines.
Please, use very important parameters in the right way:

you have to get the data, whica are divided by dependent and independetn variables;
you have to get this data in the data frame format.

Display independent variables(X) and dependent variable(y) on the screen.

Все було зрозуміло?

Дякуємо за ваш відгук!

Секція 1. Розділ 1

Loading Data Sets

One of the advantages of this library is that it already contains some basic datasets that can be used immediately. For this there are special functions in Scikit-learn. Let's look at them.

Let's get data about people with diabets. For this dataset there is load_diabetes() function in the Scikit-learn library.


              12345678
            
# Importing needed function from library
from sklearn.datasets import load_boston
# Using function fo get data about diabets from library
X, y = load_diabetes(return_X_y = True, as_frame = True)
# Displaying independent variables
print(X)
# Dislpaying dependent variable
print(y)

Here, we have very important bool parameters: return_X_y and as_frame. Exaplain.

Let's take a look at the another popular dataset about iris flowers and their features. See the example below.


              12345678
            
# Importing needed function from library
from sklearn.datasets import load_iris
# Using function fo get data about flowers from library
X, y = load_iris(return_X_y = True, as_frame = True)
# Displaying independent variables
print(X)
# Dislpaying dependent variable
print(y)

Завдання

You have to load_wine function from sklearn library.
You have to use imported function to get the data about properties and qualities of wines.
Please, use very important parameters in the right way:

you have to get the data, whica are divided by dependent and independetn variables;
you have to get this data in the data frame format.

Display independent variables(X) and dependent variable(y) on the screen.

Все було зрозуміло?

Дякуємо за ваш відгук!

One of the advantages of this library is that it already contains some basic datasets that can be used immediately. For this there are special functions in Scikit-learn. Let's look at them.

Let's get data about people with diabets. For this dataset there is load_diabetes() function in the Scikit-learn library.


              12345678
            
# Importing needed function from library
from sklearn.datasets import load_boston
# Using function fo get data about diabets from library
X, y = load_diabetes(return_X_y = True, as_frame = True)
# Displaying independent variables
print(X)
# Dislpaying dependent variable
print(y)

Here, we have very important bool parameters: return_X_y and as_frame. Exaplain.

Let's take a look at the another popular dataset about iris flowers and their features. See the example below.


              12345678
            
# Importing needed function from library
from sklearn.datasets import load_iris
# Using function fo get data about flowers from library
X, y = load_iris(return_X_y = True, as_frame = True)
# Displaying independent variables
print(X)
# Dislpaying dependent variable
print(y)

Завдання

You have to load_wine function from sklearn library.
You have to use imported function to get the data about properties and qualities of wines.
Please, use very important parameters in the right way:

you have to get the data, whica are divided by dependent and independetn variables;
you have to get this data in the data frame format.

Display independent variables(X) and dependent variable(y) on the screen.

Секція 1. Розділ 1